Iterate MP and ME requirements for individual cows

This is a recreation of some of Braeden’s work to calculate ME and MP requirements for individual cows, every day or week.

Key changes:

• uses nasem_dairy package

• avoids using a database, just keeps dataframes in memory

• simplified functions and adjusted to work with the current versions of functions that calculate ME and MP requirements

It still works by using a diet_date key to link the diet and animal tables.

TODO: replicate using full model for easier calculation of other things such as ME intake.

Code

import nasem_dairy as nd # assumes locally installed with `poetry` or from pip git+...
import pandas as pd

Code

import plotly.express as px

import plotly.io as pio

# This ensures Plotly output works in multiple places:
# plotly_mimetype: VS Code notebook UI
# notebook: "Jupyter: Export to HTML" command in VS Code
# See https://plotly.com/python/renderers/#multiple-renderers
pio.renderers.default = "plotly_mimetype+notebook"
# pio.renderers

Code

# import importlib
# nd = importlib.reload(nd)

Check input formats normally used by nasem_dairy

Code

diet_info, animal_input, equation_selection = nd.read_input('../src/nasem_dairy/data/input.txt')

Code

diet_info

	Feedstuff	%_DM_user
Index
Alfalfa meal	Alfalfa meal	16.30
Canola meal	Canola meal	10.20
Corn silage, typical	Corn silage, typical	40.00
Barley grain, dry, ground	Barley grain, dry, ground	16.32
Pasture, grass	Pasture, grass	15.00

Code

animal_input

{'An_Parity_rl': 2.0,
 'Trg_MilkProd': 35.0,
 'An_BW': 700.0,
 'An_BCS': 3.0,
 'An_LactDay': 150.0,
 'Trg_MilkFatp': 3.8,
 'Trg_MilkTPp': 3.1,
 'Trg_MilkLacp': 4.85,
 'DMI': 24.521,
 'An_BW_mature': 700.0,
 'Trg_FrmGain': 0.19,
 'An_GestDay': 46.0,
 'An_GestLength': 280.0,
 'Trg_RsrvGain': 0.0,
 'Fet_BWbrth': 44.1,
 'An_AgeDay': 1620.0,
 'An_305RHA_MlkTP': 280.0}

Code

equation_selection

{'Use_DNDF_IV': 0, 'DMI_pred': 1}

Import data from multiple cows/diets

This file was put together roughly from Elora. Each row is a cow’s measured performance as well as the combined TMR nutrient analysis of what she was likely eating. It is used to estimate MP and ME requirements of individuals.

Code

# Create dataframe with test data
input_data = pd.read_parquet('./test_files/merged_cow_with_feed_daily_20230530_1557.parquet')
# Remove animals missing diet information
input_data_dropna = input_data.dropna(subset=['sampleId'])
input_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17599 entries, 0 to 17598
Data columns (total 49 columns):
 #   Column                          Non-Null Count  Dtype         
---  ------                          --------------  -----         
 0   cow_id                          17599 non-null  int64         
 1   lactation_number                17599 non-null  int64         
 2   date                            17599 non-null  datetime64[ns]
 3   days_in_milk                    17599 non-null  float64       
 4   MY                              17599 non-null  float64       
 5   weight                          17599 non-null  float64       
 6   BW_smooth                       17599 non-null  float64       
 7   BW_gain                         17599 non-null  float64       
 8   asfed_intake                    17599 non-null  float64       
 9   DMI                             17599 non-null  float64       
 10  bcs_value                       6377 non-null   float64       
 11  Birth Date                      15891 non-null  datetime64[ns]
 12  Test Day Date                   15891 non-null  datetime64[ns]
 13  Lact Start Date                 15891 non-null  datetime64[ns]
 14  Fat %                           15891 non-null  float64       
 15  Protein %                       15891 non-null  float64       
 16  SCC                             15891 non-null  float64       
 17  Pregnancy Indicator             15891 non-null  object        
 18  Days to Last Breeding           15891 non-null  float64       
 19  days_preg                       15891 non-null  float64       
 20  conception_date                 3779 non-null   datetime64[ns]
 21  age_m                           15891 non-null  float64       
 22  sampleId                        15941 non-null  object        
 23  reportDate                      15941 non-null  datetime64[ns]
 24  location                        15941 non-null  object        
 25  Acid Detergent Fibre (%)        15941 non-null  float64       
 26  Ash (%)                         15941 non-null  float64       
 27  Calcium (%)                     15941 non-null  float64       
 28  Copper (ug/g)                   15941 non-null  float64       
 29  Crude Fat (%)                   15941 non-null  float64       
 30  Crude Protein (%)               15941 non-null  float64       
 31  Dry Matter (%)                  15941 non-null  float64       
 32  Iron (ug/g)                     15941 non-null  float64       
 33  Magnesium (%)                   15941 non-null  float64       
 34  Manganese (ug/g)                15941 non-null  float64       
 35  Moisture (%)                    15941 non-null  float64       
 36  NE Gain (MCal/Kg)               15941 non-null  float64       
 37  NE Lactation (MCal/Kg)          15941 non-null  float64       
 38  NE Maintenance (MCal/Kg)        15941 non-null  float64       
 39  NFC (%)                         15941 non-null  float64       
 40  Neutral Detergent Fibre (%)     15941 non-null  float64       
 41  Phosphorus (%)                  15941 non-null  float64       
 42  Potassium (%)                   15941 non-null  float64       
 43  Sodium (%)                      15941 non-null  float64       
 44  Starch (%)                      15941 non-null  float64       
 45  Sulphur (%)                     15941 non-null  float64       
 46  Total Digestible Nutrients (%)  15941 non-null  float64       
 47  Zinc (ug/g)                     15941 non-null  float64       
 48  DIM_bins_w                      17599 non-null  category      
dtypes: category(1), datetime64[ns](6), float64(37), int64(2), object(3)
memory usage: 6.5+ MB

Code

# Cleaning Data: Step 1
# Move diet information to the database and replace with unique Diet_ID

def get_diets(df):
    '''
    This assumes an input df with weekly or daily entries of cow data + diet data. This function extracts the diet component, 
    adding a key that will be linked to the animal data

    df: The output from combine_dairy_data_python/dev/dev_format_feed_quality
    '''
    current_diets = df.assign(
    Diet_ID = lambda df: df['sampleId'] + '_' + df['reportDate'].dt.strftime('%Y-%m-%d')
    ).drop(
    columns = ['lactation_number', 'days_in_milk', 'MY', 'weight', 'BW_smooth', 'BW_gain', 'asfed_intake', 'DMI', 'bcs_value', 'Birth Date', 'Test Day Date',
                    'Lact Start Date', 'Fat %', 'Protein %', 'SCC', 'Pregnancy Indicator', 'Days to Last Breeding', 'days_preg', 'conception_date', 'age_m', 'cow_id', 'date', 'DIM_bins_w']
    ).drop_duplicates(
    subset = ['sampleId', 'reportDate'],
    keep = 'first'
    )


    return current_diets


current_diets = get_diets(input_data_dropna)

current_diets

	sampleId	reportDate	location	Acid Detergent Fibre (%)	Ash (%)	Calcium (%)	Copper (ug/g)	Crude Fat (%)	Crude Protein (%)	Dry Matter (%)	...	NFC (%)	Neutral Detergent Fibre (%)	Phosphorus (%)	Potassium (%)	Sodium (%)	Starch (%)	Sulphur (%)	Total Digestible Nutrients (%)	Zinc (ug/g)	Diet_ID
1658	DAIRY - LACTATING TMR	2021-01-25	parlour	21.23	6.56	0.77	12.38	3.08	14.63	47.34	...	43.59	30.28	0.37	1.19	0.32	25.88	0.13	72.36	61.85	DAIRY - LACTATING TMR_2021-01-25
2290	DAIRY-LACTATING TMR	2021-02-04	parlour	21.12	6.48	0.79	13.88	3.12	14.20	46.93	...	41.28	33.02	0.46	1.46	0.38	27.17	0.15	72.45	102.66	DAIRY-LACTATING TMR_2021-02-04
4039	DARIRY LACTATING TMR	2021-03-05	parlour	19.68	5.92	0.79	14.81	3.30	14.01	45.79	...	41.04	33.45	0.42	1.39	0.31	28.60	0.14	73.57	97.36	DARIRY LACTATING TMR_2021-03-05
5807	DAIRY NON RUMENSIN LACTATING TMR	2021-04-08	parlour	23.00	6.43	0.86	12.37	3.39	17.86	44.95	...	38.85	31.79	0.46	1.54	0.32	22.63	0.18	70.98	116.44	DAIRY NON RUMENSIN LACTATING TMR_2021-04-08
7063	DAIRY LACTATING TMR	2021-05-06	parlour	22.01	6.67	0.86	12.85	3.22	16.17	47.82	...	40.51	31.82	0.41	1.32	0.39	25.08	0.17	71.75	104.39	DAIRY LACTATING TMR_2021-05-06
8811	NON RUM LACT TMR-DAIRY	2021-06-04	parlour	19.97	6.38	0.77	14.90	3.18	14.10	47.39	...	44.79	29.61	0.38	1.34	0.39	22.79	0.14	73.34	85.10	NON RUM LACT TMR-DAIRY_2021-06-04
10236	LACTATING TMR-DAIRY	2021-07-02	parlour	22.12	6.63	1.03	16.08	3.12	14.89	47.23	...	43.84	29.77	0.47	1.48	0.41	23.92	0.19	71.67	99.75	LACTATING TMR-DAIRY_2021-07-02
12108	DAIRY LACTATING TMR	2021-08-06	parlour	20.16	6.75	0.78	16.37	3.44	15.51	49.65	...	44.74	28.25	0.36	1.35	0.38	24.17	0.18	73.20	107.76	DAIRY LACTATING TMR_2021-08-06
13628	DAIRY LACTATING TMR	2021-09-03	parlour	21.48	7.05	0.86	14.69	3.75	15.12	44.47	...	42.24	31.14	0.47	1.41	0.29	23.52	0.19	72.17	114.80	DAIRY LACTATING TMR_2021-09-03
14339	DAIRY LACTATING TMR	2021-10-12	parlour	21.02	6.75	0.96	15.07	2.68	15.75	47.36	...	41.78	30.97	0.42	1.22	0.34	22.64	0.18	72.53	86.65	DAIRY LACTATING TMR_2021-10-12
14764	DAIRY LACTATING TMR	2021-11-09	parlour	22.01	6.78	0.95	23.52	3.20	18.40	46.07	...	39.59	30.51	0.42	1.46	0.33	31.29	0.23	71.75	150.05	DAIRY LACTATING TMR_2021-11-09
16404	DAIRY LACTATING TMR	2021-12-10	parlour	21.96	7.28	0.93	17.54	3.26	15.21	46.16	...	43.41	29.88	0.40	1.76	0.41	21.65	0.26	71.79	119.15	DAIRY LACTATING TMR_2021-12-10

12 rows × 27 columns

Code

def clean_animal_inputs(df):
    '''
    This assumes an input df with weekly or daily entries of cow data + diet data. 
    This function extracts the animal component, 
    adding a key that will be linked to the diet data

    df: The output from combine_dairy_data_python/dev/dev_format_feed_quality
    '''
    # Rename existing columns
    # From farm names to names that match those required downstream
    df = df.rename(columns={
        'lactation_number': 'An_Parity_rl',
        'days_in_milk': 'An_LactDay',
        'MY': 'Trg_MilkProd',
        'BW_smooth': 'An_BW',
        # 'DMI': 'Dt_DMIn', # downstream & execute_model.py use 'DMI'
        'Fat %': 'Trg_MilkFatp',
        'Protein %': 'Trg_MilkTPp',
        'days_preg': 'An_GestDay'
        })
    
    # Add default values and drop columns we don't need
    clean_data = df.assign(
            An_BW_mature = 700,
            Trg_FrmGain = 0,
            An_GestLength = 280,
            Fet_BWbrth = 44.1,
            Trg_MilkLacp = 4.85,
            Trg_RsrvGain = 0,
            An_AgeDay = lambda x: x['age_m'] * 30.436875,

            # add NDF intake (before dropping animal columns from df)
            Dt_NDFIn = lambda df: df['Neutral Detergent Fibre (%)']/100 * df['DMI']
    ).drop(
        columns = ['location', 'Acid Detergent Fibre (%)', 'Ash (%)', 'Calcium (%)', 'Copper (ug/g)', 'Crude Fat (%)', 'Crude Protein (%)', 'Dry Matter (%)', 'Iron (ug/g)', 'Magnesium (%)', 'Manganese (ug/g)', 'Moisture (%)', 'NE Gain (MCal/Kg)', 'NE Lactation (MCal/Kg)', 'NE Maintenance (MCal/Kg)', 'NFC (%)', 'Neutral Detergent Fibre (%)', 'Phosphorus (%)', 'Potassium (%)', 'Sodium (%)', 'Starch (%)', 'Sulphur (%)', 'Total Digestible Nutrients (%)', 'Zinc (ug/g)', 'DIM_bins_w']
    ).assign(
        # add key column to match diet data:
        Diet_ID = lambda df: df['sampleId'] + '_' + df['reportDate'].dt.strftime('%Y-%m-%d')
    )


    return clean_data

animal_data = clean_animal_inputs(input_data_dropna)
print(animal_data.iloc[:, :6].head(2))

      cow_id  An_Parity_rl       date  An_LactDay  Trg_MilkProd  weight
1658    4921             1 2021-01-25       140.0         27.81   663.0
1659    4823             2 2021-01-25        72.0         31.97   734.0

Code

animal_data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 15941 entries, 1658 to 17598
Data columns (total 33 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   cow_id                 15941 non-null  int64         
 1   An_Parity_rl           15941 non-null  int64         
 2   date                   15941 non-null  datetime64[ns]
 3   An_LactDay             15941 non-null  float64       
 4   Trg_MilkProd           15941 non-null  float64       
 5   weight                 15941 non-null  float64       
 6   An_BW                  15941 non-null  float64       
 7   BW_gain                15941 non-null  float64       
 8   asfed_intake           15941 non-null  float64       
 9   DMI                    15941 non-null  float64       
 10  bcs_value              4759 non-null   float64       
 11  Birth Date             14681 non-null  datetime64[ns]
 12  Test Day Date          14681 non-null  datetime64[ns]
 13  Lact Start Date        14681 non-null  datetime64[ns]
 14  Trg_MilkFatp           14681 non-null  float64       
 15  Trg_MilkTPp            14681 non-null  float64       
 16  SCC                    14681 non-null  float64       
 17  Pregnancy Indicator    14681 non-null  object        
 18  Days to Last Breeding  14681 non-null  float64       
 19  An_GestDay             14681 non-null  float64       
 20  conception_date        3415 non-null   datetime64[ns]
 21  age_m                  14681 non-null  float64       
 22  sampleId               15941 non-null  object        
 23  reportDate             15941 non-null  datetime64[ns]
 24  An_BW_mature           15941 non-null  int64         
 25  Trg_FrmGain            15941 non-null  int64         
 26  An_GestLength          15941 non-null  int64         
 27  Fet_BWbrth             15941 non-null  float64       
 28  Trg_MilkLacp           15941 non-null  float64       
 29  Trg_RsrvGain           15941 non-null  int64         
 30  An_AgeDay              14681 non-null  float64       
 31  Dt_NDFIn               15941 non-null  float64       
 32  Diet_ID                15941 non-null  object        
dtypes: datetime64[ns](6), float64(18), int64(6), object(3)
memory usage: 4.1+ MB

Code

# combined code to calculate GrUter_BWgain, ME and MP requirements.
# In order to work it needed to use the df.apply method, but then multiple columns can still be assigned using .assign.
# Even though it's not ideal to run the apply inside each call to assign, it's still fairly clean to read.
calculated_df = animal_data.assign(
        GrUter_BWgain = lambda df: df.apply( lambda x:
                nd.calculate_GrUter_BWgain(
                        x['Fet_BWbrth'], 
                        x['An_AgeDay'], 
                        x['An_GestDay'], 
                        x['An_GestLength'], 
                        x['An_LactDay'], 
                        x['An_Parity_rl'], 
                        coeff_dict = nd.coeff_dict),
                axis = 1 #row wise
        ),
        ME_requirement = lambda df: df.apply( lambda x: 
                nd.calculate_ME_requirement(
                        x['An_BW'], 
                        x['DMI'], 
                        x['Trg_MilkProd'], 
                        x['An_BW_mature'], 
                        x['Trg_FrmGain'],
                        x['Trg_MilkFatp'], 
                        x['Trg_MilkTPp'], 
                        x['Trg_MilkLacp'], 
                        x['Trg_RsrvGain'], 
                        x['GrUter_BWgain'], 
                        coeff_dict = nd.coeff_dict)[0], #Trg_MEuse
                axis = 1
                ),
        MP_requirement = lambda df: df.apply( lambda x: 
                nd.calculate_MP_requirement(
                        x['Dt_NDFIn'], 
                        x['DMI'], 
                        x['An_BW'], 
                        x['An_BW_mature'], 
                        x['Trg_FrmGain'],
                        x['Trg_RsrvGain'], 
                        x['Trg_MilkProd'], 
                        x['Trg_MilkTPp'], 
                        x['GrUter_BWgain'], 
                        coeff_dict = nd.coeff_dict)[0], #An_MPuse_g_Trg
                axis = 1
                )
        )

Code

# Check for cows missing data

def check_na_requirement(df):    
    columns_to_check = ['ME_requirement', 'MP_requirement']
    check_na = df[columns_to_check].isna().any(axis=1)
    cows_missing_data = df[check_na]
    return cows_missing_data

cows_missing_data = check_na_requirement(calculated_df)
cows_missing_data

	cow_id	An_Parity_rl	date	An_LactDay	Trg_MilkProd	weight	An_BW	BW_gain	asfed_intake	DMI	...	An_GestLength	Fet_BWbrth	Trg_MilkLacp	Trg_RsrvGain	An_AgeDay	Dt_NDFIn	Diet_ID	GrUter_BWgain	ME_requirement	MP_requirement
1678	4824	2	2021-01-25	9.0	50.34	756.0	761.967	-1.854	39.6	17.820	...	280	44.1	4.85	0	NaN	5.395896	DAIRY - LACTATING TMR_2021-01-25	0.0	NaN	NaN
1694	4962	1	2021-01-25	14.0	30.81	556.0	549.603	-1.854	31.4	14.130	...	280	44.1	4.85	0	NaN	4.278564	DAIRY - LACTATING TMR_2021-01-25	0.0	NaN	NaN
1703	4679	3	2021-01-25	17.0	48.46	822.0	828.111	-0.308	40.8	18.360	...	280	44.1	4.85	0	NaN	5.559408	DAIRY - LACTATING TMR_2021-01-25	0.0	NaN	NaN
1708	4715	3	2021-01-25	13.0	31.00	626.0	636.165	1.984	44.8	20.160	...	280	44.1	4.85	0	NaN	6.104448	DAIRY - LACTATING TMR_2021-01-25	0.0	NaN	NaN
1728	4715	3	2021-01-26	14.0	30.83	640.0	638.095	1.971	29.2	13.140	...	280	44.1	4.85	0	NaN	3.978792	DAIRY - LACTATING TMR_2021-01-25	0.0	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
17562	4794	3	2021-12-31	20.0	46.18	710.0	708.289	-3.033	41.5	18.675	...	280	44.1	4.85	0	NaN	5.580090	DAIRY LACTATING TMR_2021-12-10	0.0	NaN	NaN
17573	4966	2	2021-12-31	24.0	35.23	635.0	619.042	-2.705	42.9	19.305	...	280	44.1	4.85	0	NaN	5.768334	DAIRY LACTATING TMR_2021-12-10	0.0	NaN	NaN
17575	4967	2	2021-12-31	10.0	32.53	640.0	671.499	-2.391	48.9	22.005	...	280	44.1	4.85	0	NaN	6.575094	DAIRY LACTATING TMR_2021-12-10	0.0	NaN	NaN
17578	4943	2	2021-12-31	23.0	25.01	801.0	779.264	-1.451	44.6	20.070	...	280	44.1	4.85	0	NaN	5.996916	DAIRY LACTATING TMR_2021-12-10	0.0	NaN	NaN
17592	4711	4	2021-12-31	18.0	36.99	740.0	824.037	-3.974	41.7	18.765	...	280	44.1	4.85	0	NaN	5.606982	DAIRY LACTATING TMR_2021-12-10	0.0	NaN	NaN

1260 rows × 36 columns

Code

calculated_df_noNA = calculated_df.dropna(subset = ['ME_requirement', 'MP_requirement'])

Code

calculated_df_noNA.info()

<class 'pandas.core.frame.DataFrame'>
Index: 14681 entries, 1658 to 17598
Data columns (total 36 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   cow_id                 14681 non-null  int64         
 1   An_Parity_rl           14681 non-null  int64         
 2   date                   14681 non-null  datetime64[ns]
 3   An_LactDay             14681 non-null  float64       
 4   Trg_MilkProd           14681 non-null  float64       
 5   weight                 14681 non-null  float64       
 6   An_BW                  14681 non-null  float64       
 7   BW_gain                14681 non-null  float64       
 8   asfed_intake           14681 non-null  float64       
 9   DMI                    14681 non-null  float64       
 10  bcs_value              4576 non-null   float64       
 11  Birth Date             14681 non-null  datetime64[ns]
 12  Test Day Date          14681 non-null  datetime64[ns]
 13  Lact Start Date        14681 non-null  datetime64[ns]
 14  Trg_MilkFatp           14681 non-null  float64       
 15  Trg_MilkTPp            14681 non-null  float64       
 16  SCC                    14681 non-null  float64       
 17  Pregnancy Indicator    14681 non-null  object        
 18  Days to Last Breeding  14681 non-null  float64       
 19  An_GestDay             14681 non-null  float64       
 20  conception_date        3415 non-null   datetime64[ns]
 21  age_m                  14681 non-null  float64       
 22  sampleId               14681 non-null  object        
 23  reportDate             14681 non-null  datetime64[ns]
 24  An_BW_mature           14681 non-null  int64         
 25  Trg_FrmGain            14681 non-null  int64         
 26  An_GestLength          14681 non-null  int64         
 27  Fet_BWbrth             14681 non-null  float64       
 28  Trg_MilkLacp           14681 non-null  float64       
 29  Trg_RsrvGain           14681 non-null  int64         
 30  An_AgeDay              14681 non-null  float64       
 31  Dt_NDFIn               14681 non-null  float64       
 32  Diet_ID                14681 non-null  object        
 33  GrUter_BWgain          14681 non-null  float64       
 34  ME_requirement         14681 non-null  float64       
 35  MP_requirement         14681 non-null  float64       
dtypes: datetime64[ns](6), float64(21), int64(6), object(3)
memory usage: 4.1+ MB

Code

px.scatter(
    calculated_df_noNA,
    x='DMI', y='ME_requirement',
    color='An_LactDay')

Code

px.scatter(
    calculated_df_noNA,
    x='An_LactDay', y='ME_requirement',
    color='An_LactDay')

Code

px.scatter(
    calculated_df_noNA,
    x='An_LactDay', y='MP_requirement', hover_data = ['cow_id', 'date'],
    color='An_LactDay')

Code

px.scatter(
    calculated_df_noNA,
    x='MP_requirement', y='ME_requirement') 

Code

# get ME intakes

# calculated_df_noNA.info()
current_diets.info()

<class 'pandas.core.frame.DataFrame'>
Index: 12 entries, 1658 to 16404
Data columns (total 27 columns):
 #   Column                          Non-Null Count  Dtype         
---  ------                          --------------  -----         
 0   sampleId                        12 non-null     object        
 1   reportDate                      12 non-null     datetime64[ns]
 2   location                        12 non-null     object        
 3   Acid Detergent Fibre (%)        12 non-null     float64       
 4   Ash (%)                         12 non-null     float64       
 5   Calcium (%)                     12 non-null     float64       
 6   Copper (ug/g)                   12 non-null     float64       
 7   Crude Fat (%)                   12 non-null     float64       
 8   Crude Protein (%)               12 non-null     float64       
 9   Dry Matter (%)                  12 non-null     float64       
 10  Iron (ug/g)                     12 non-null     float64       
 11  Magnesium (%)                   12 non-null     float64       
 12  Manganese (ug/g)                12 non-null     float64       
 13  Moisture (%)                    12 non-null     float64       
 14  NE Gain (MCal/Kg)               12 non-null     float64       
 15  NE Lactation (MCal/Kg)          12 non-null     float64       
 16  NE Maintenance (MCal/Kg)        12 non-null     float64       
 17  NFC (%)                         12 non-null     float64       
 18  Neutral Detergent Fibre (%)     12 non-null     float64       
 19  Phosphorus (%)                  12 non-null     float64       
 20  Potassium (%)                   12 non-null     float64       
 21  Sodium (%)                      12 non-null     float64       
 22  Starch (%)                      12 non-null     float64       
 23  Sulphur (%)                     12 non-null     float64       
 24  Total Digestible Nutrients (%)  12 non-null     float64       
 25  Zinc (ug/g)                     12 non-null     float64       
 26  Diet_ID                         12 non-null     object        
dtypes: datetime64[ns](1), float64(23), object(3)
memory usage: 2.6+ KB